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WO2018162021A1 - Procédé d'estimation d'état de charge de cellule de batterie et système de surveillance d'état de batterie - Google Patents

Procédé d'estimation d'état de charge de cellule de batterie et système de surveillance d'état de batterie Download PDF

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Publication number
WO2018162021A1
WO2018162021A1 PCT/EP2017/055126 EP2017055126W WO2018162021A1 WO 2018162021 A1 WO2018162021 A1 WO 2018162021A1 EP 2017055126 W EP2017055126 W EP 2017055126W WO 2018162021 A1 WO2018162021 A1 WO 2018162021A1
Authority
WO
WIPO (PCT)
Prior art keywords
soc
battery cell
voltage
current
measured
Prior art date
Application number
PCT/EP2017/055126
Other languages
English (en)
Inventor
Esteban GELSO
Original Assignee
Volvo Truck Corporation
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Volvo Truck Corporation filed Critical Volvo Truck Corporation
Priority to CN201780088001.8A priority Critical patent/CN110418972A/zh
Priority to PCT/EP2017/055126 priority patent/WO2018162021A1/fr
Priority to US16/486,997 priority patent/US11320491B2/en
Priority to EP17708792.1A priority patent/EP3593155B1/fr
Publication of WO2018162021A1 publication Critical patent/WO2018162021A1/fr

Links

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385Arrangements for measuring battery or accumulator variables
    • G01R31/387Determining ampere-hour charge capacity or SoC
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60LPROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367Software therefor, e.g. for battery testing using modelling or look-up tables
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00Road transport of goods or passengers
    • Y02T10/60Other road transportation technologies with climate change mitigation effect
    • Y02T10/70Energy storage systems for electromobility, e.g. batteries

Definitions

  • the invention relates to a method for robust estimation of state of charge (SOC) for a battery cell.
  • the invention further relates to a computer program comprising program code performing the steps of the method, a computer readable medium carrying such a computer program, a control unit (2) for controlling the monitoring the state of a battery cell, a battery state monitoring system, and an electrical vehicle comprising such a battery state monitoring system.
  • the electrical vehicle may be heavy-duty vehicles, such as trucks, buses and construction equipment, but may also be used in other vehicles such as smaller electrical industrial vehicles, and passenger cars.
  • Electrochemical storage devices as batteries are important in modern energy infrastructure. Many different types of equipment rely on battery energy storage. In the transportation industry batteries have always been used for service purposes in vehicles with combustion engines, but as the industry develops electrical propulsion systems, the requirements of energy storage in batteries increase. Charging of batteries for electrical vehicles have to be quick, safe and reliable. Batteries are larger, has to deliver more power and are used in a more demanding way with more frequent and deeper discharges. In advanced systems as electrical vehicles accurate estimation of the state of charge of a (SOC) battery is important. State of charge (SOC) is an important variable to prevent batteries from under- or over-charging situations, and to manage the energy in electric vehicles. SOC needs to be estimated since no direct measurement is available SOC can be difficult to estimate correctly using systems and methods of the prior art.
  • a well-known method for estimating SOC is based on a Kalman filter, which uses a battery cell model.
  • the cell terminal voltage is the output and the cell current and cell temperature are the inputs.
  • the SOC estimated by the model is corrected.
  • a method based on the Kalman filter introducing possible improvements of SOC estimation is presented in US 2014/0244193 A1 introducing an iterative method using a filter for improving the precision of SOC estimation. The presented method, however, only addresses problems with precision but does not address accuracy problems in the SOC estimation. There is thus a need for improved methods, systems and devices for estimation of the SOC of a battery.
  • An object of the invention is to improve the current state of the art, to solve the above problems, and to provide an improved method for robust estimation of state of charge (SOC) for a battery cell, e.g. for an electric vehicle.
  • SOC state of charge
  • the method comprises measuring an output current from the battery cell; a temperature of the battery cell; and an output voltage from the battery cell; providing a SOC estimation model for the battery cell comprising the measured current and the measured temperature to provide an estimated output voltage; calculating the estimated output voltage and an initial SOC value using the SOC estimation model; calculating a voltage difference between the estimated output voltage and the measured voltage; and estimating the SOC for a battery cell by optimizing said SOC estimation model based on the calculated voltage difference and the initial SOC value.
  • the method is characterized in that the SOC estimation model further comprises a current fault estimate for an error of the measured current; and/or the SOC estimation model further comprises a voltage fault estimate for an error of a measured output voltage; and in that the step of estimating the SOC for a battery cell is further optimized based on the current fault estimate and/or the voltage fault estimate.
  • the error of the measured current may be based on errors such as bias or drift in the current sensor and the error of the measured voltage may be based on errors such as bias or drift in the voltage sensor or intermittent sensor faults.
  • the method of the present invention solves these problems and estimates a SOC that is robust to faults in voltage and current sensors.
  • the inventive method minimizes the accuracy degradation by those faults.
  • the SOC estimation model may further be based on a Kalman filter model and the step of optimizing the SOC estimation may be the measurement update step of the Kalman filter model (the "correcf-step). Basing the method on a Kalman filter will further to the increased accuracy also introduce handling of noise in the SOC estimation that is affecting the precision of the results.
  • a computer program comprising program code means for performing the steps of the method described herein, when the computer program is run on a computer.
  • the objects are achieved by a computer readable medium carrying the aforementioned computer program comprising program code means for performing the method, when the program product is run on a computer.
  • the objects are achieved by a control unit for controlling the monitoring of the state of a battery cell, the control unit comprising a circuit configured to perform a robust estimation of state of charge for a battery cell, wherein the control unit is arranged to perform the steps of the herein discussed method.
  • the objects are achieved by a battery state monitoring system for monitoring the state of a battery cell; comprising a temperature sensor arranged to sense the temperature of said battery cell, or a temperature sensor located in the vicinity of the battery cell used to estimate the battery cell temperature; a current sensor arranged to measure the output current from said battery cell; a voltage sensor arranged to measure the output current from said battery cell; and a control unit as described above.
  • a battery state monitoring system for monitoring the state of a battery cell; comprising a temperature sensor arranged to sense the temperature of said battery cell, or a temperature sensor located in the vicinity of the battery cell used to estimate the battery cell temperature; a current sensor arranged to measure the output current from said battery cell; a voltage sensor arranged to measure the output current from said battery cell; and a control unit as described above.
  • the objects are achieved by an electrical vehicle comprising such a battery state monitoring system.
  • Fig. 1 is a schematic view of a circuit performing the inventive method for estimating the SOC for a battery cell from measured values of the battery output current (l m ), temperature ( T m ), and output voltage
  • Fig. 2 is a schematic view of a battery state monitoring system for monitoring the state of a battery cell com prising the circuit of Fig. 1 in a control unit, sensors for measuring battery properties.
  • Fig. 3 is block diagram showing the inventive method for estimating the SOC for a battery cell.
  • Fig. 4 is schematic view of an electrical vehicle comprising the battery state monitoring system of Fig. 3.
  • Fig. 1 is a schematic view of a circuit 1 performing the inventive method M for estimating the SOC for a battery cell from measured values of the battery output current l m , temperature T m , and output voltage y.
  • An intermediate SOC value (SOC int ), a calculated output voltage, a current fault estimate l f and a voltage fault estimate are iterated in the
  • SOC estimated SOC value
  • Fig. 2 is a schematic view of a battery state monitoring system 10 for monitoring the state of a battery cell 6 comprising a control unit containing the circuit 1 of Fig. 1 .
  • a voltage sensor 5 measures the output voltage of the battery cell 6
  • a current sensor 4 measures the current of the battery cell 6
  • a temperature sensor 3 measures the temperature of the battery 6 cell.
  • a SOC estimation model M is provided for the battery cell to provide an estimated output voltage y, the model comprising the measured current l m and the measured temperature T m , a current fault estimate (l f ) and a voltage fault estimate (y f ).
  • the method is calculating the estimated output voltage and an intermediate SOC value SOC int using the SOC estimation model M.
  • the method is Estimating the SOC for a battery cell by optimizing said SOC estimation model based on the calculated voltage difference, the current fault estimate, the voltage fault estimate, and the initial SOC value.
  • Fig. 4 is schematic view of an electrical vehicle 20 comprising the battery state monitoring system 10 shown in Fig. 3 connected to a battery cell 6 of the electrical vehicle.
  • the inventive method will now be discussed more in detail with exemplifying mathematic expressions for carrying out the method.
  • the SOC estimator is designed to be robust to faults or errors in voltage and current sensors.
  • the model M is extended by an additional state (x 4 ) to consider faults like a bias or a drift in the current sensor; and an additional variable ⁇ (k) is added to model faults in the cell terminal voltage sensor.
  • x 3 is the estimated SOC
  • x 4 is the current fault estimate ⁇ h
  • z(k) is the voltage fault estimate
  • Ci and C 2 are capacitances, and Ri and R 2 are resistances, of the RC branches of the equivalent circuit model of the battery cell
  • is the Coulombic efficiency of the battery cell
  • T s is the sampling time
  • C n is the battery cell capacity
  • w is the process noise.
  • x 7 is the SOC
  • x L and 3 ⁇ 4 are the voltages in the RC branches.
  • the output voltage is defined as:
  • R 0 is the ohmic resistance
  • OCV is the open circuit voltage, which in this case is a function of the SOC.
  • v is the observation noise. It can also be written in a more compact way as:
  • variants that is they can change the value with time and, for example, with the battery cell temperature.
  • the parameters can also change with the current SOC value, and the battery cell output current.
  • the SOC estimator which could be for example a Kalman filter, is used with the described 2RC circuit model. Faults in the cell terminal voltage sensor of the SOC estimator degrades the accuracy of the SOC estimator. Examples of faults include a bias, a drift, or a random spike (outlier). In the inventive method, they are modeled as a variable z c) , which is now included in the battery cell terminal voltage y(fe) as:
  • zOc is minimized in an optimization problem solved every time instant, such that the error between the voltage measurement and the battery cell model prediction is minimized.
  • c. , c. , and a are tuning parameters greater than 0, and G is equal to:
  • G is equal to:
  • the Predict step also called Time Update
  • the intermediate SOC value SOC int is the intermediate SOC value SOC int .

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Secondary Cells (AREA)

Abstract

L'invention concerne un procédé d'estimation robuste de l'état de charge (SOC) d'une cellule de batterie (6) d'un véhicule électrique, le procédé comprenant les étapes consistant à : mesurer un courant de sortie (/m) issu de la cellule de batterie ; une température (Tm) de la cellule de batterie ; et une tension de sortie (y) de la cellule de batterie ; produire un modèle d'estimation de SOC (M) pour la cellule de batterie comprenant le courant mesuré (/m) et la température mesurée (Tm) pour fournir une tension de sortie estimée (y) ; calculer la tension de sortie estimée (y) et une valeur de SOC intermédiaire (SOCint) à l'aide du modèle d'estimation de SOC (M) ; calculer une différence de tension entre la tension de sortie estimée (y) et la tension mesurée (y) ; estimer le SOC (SOC) pour une cellule de batterie en optimisant ledit modèle d'estimation de SOC (M) d'après la différence de tension calculée et la valeur de SOC intermédiaire (SOCint). Le procédé est caractérisé en ce que le modèle d'estimation de SOC (M) comprend également une estimation de défaut de courant (lf) pour une erreur du courant mesuré (/m) ; et/ou en ce que le modèle d'estimation de SOC (M) comprend également une estimation de défaut de tension (yf) pour une erreur d'une tension de sortie mesurée (ym) ; et en ce que l'étape d'estimation du SOC (SOC) d'une cellule de batterie est également optimisée d'après l'estimation de défaut de courant (lf) et/ou l'estimation de défaut de tension (yf). L'invention concerne également un programme informatique comprenant un code de programme exécutant les étapes du procédé, un support lisible par ordinateur contenant ce programme informatique, une unité de commande (2) servant à commander la surveillance de l'état d'une cellule de batterie, un système de surveillance d'état de batterie et un véhicule électrique équipé de ce système de surveillance d'état de batterie.
PCT/EP2017/055126 2017-03-06 2017-03-06 Procédé d'estimation d'état de charge de cellule de batterie et système de surveillance d'état de batterie WO2018162021A1 (fr)

Priority Applications (4)

Application Number Priority Date Filing Date Title
CN201780088001.8A CN110418972A (zh) 2017-03-06 2017-03-06 电池单体充电状态估计方法和电池状态监测系统
PCT/EP2017/055126 WO2018162021A1 (fr) 2017-03-06 2017-03-06 Procédé d'estimation d'état de charge de cellule de batterie et système de surveillance d'état de batterie
US16/486,997 US11320491B2 (en) 2017-03-06 2017-03-06 Battery cell state of charge estimation method and a battery state monitoring system
EP17708792.1A EP3593155B1 (fr) 2017-03-06 2017-03-06 Procédé d'estimation d'état de charge de cellule de batterie et système de surveillance d'état de batterie

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/EP2017/055126 WO2018162021A1 (fr) 2017-03-06 2017-03-06 Procédé d'estimation d'état de charge de cellule de batterie et système de surveillance d'état de batterie

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WO2018162021A1 true WO2018162021A1 (fr) 2018-09-13

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US (1) US11320491B2 (fr)
EP (1) EP3593155B1 (fr)
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CN110843603A (zh) * 2019-10-31 2020-02-28 上海思致汽车工程技术有限公司 基于传感器的电动汽车低压电源温控管理的方法和系统
EP3640652A1 (fr) * 2018-10-15 2020-04-22 Continental Automotive GmbH Procédé de fonctionnement d'un capteur de batterie et capteur de batterie
WO2021000905A1 (fr) * 2019-07-03 2021-01-07 华人运通(江苏)技术有限公司 Procédé de surveillance d'état de batterie, processeur périphérique, système et support d'informations
CN112816876A (zh) * 2020-12-28 2021-05-18 湖南航天捷诚电子装备有限责任公司 一种用于可充电电池的低温电池剩余电量估算方法及装置
CN113009371A (zh) * 2021-03-16 2021-06-22 安徽江淮汽车集团股份有限公司 电池包电压紊乱故障判断方法、装置、设备及存储介质

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EP3640652A1 (fr) * 2018-10-15 2020-04-22 Continental Automotive GmbH Procédé de fonctionnement d'un capteur de batterie et capteur de batterie
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CN110843603A (zh) * 2019-10-31 2020-02-28 上海思致汽车工程技术有限公司 基于传感器的电动汽车低压电源温控管理的方法和系统
CN112816876A (zh) * 2020-12-28 2021-05-18 湖南航天捷诚电子装备有限责任公司 一种用于可充电电池的低温电池剩余电量估算方法及装置
CN112816876B (zh) * 2020-12-28 2021-12-07 湖南航天捷诚电子装备有限责任公司 一种用于可充电电池的低温电池剩余电量估算方法及装置
CN113009371A (zh) * 2021-03-16 2021-06-22 安徽江淮汽车集团股份有限公司 电池包电压紊乱故障判断方法、装置、设备及存储介质
CN113009371B (zh) * 2021-03-16 2022-03-04 安徽江淮汽车集团股份有限公司 电池包电压紊乱故障判断方法、装置、设备及存储介质

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US11320491B2 (en) 2022-05-03
CN110418972A (zh) 2019-11-05
US20200018797A1 (en) 2020-01-16
EP3593155B1 (fr) 2021-04-07
EP3593155A1 (fr) 2020-01-15

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